Link to DOI – 10.1109/ISBI48211.2021.9433987
In developmental biology, quantification of cellular morphological features is a critical step to get insight into tissue morphogenesis. The efficacy of the quantification tools rely heavily on robust segmentation techniques which can delineate individual cells/nuclei from cluttered environment. Application of popular neural network methods is restrained by the availability of ground truth necessary for 3D nuclei segmentation. Consequently, we propose a convolutional neural network method, combined with graph theoretic approach for 3D nuclei segmentation of mouse embryo cardiomyocytes, imaged by light-sheet microscopy. The designed neural network architecture encapsulates both membrane and nuclei cues for 2D detection. A global association of the 2D nuclei detection is performed by solving a linear optimization with second order constraint to obtain 3D nuclei reconstruction.